from torch import nn
from collections import OrderedDict
from ..builder import BACKBONES
from mmcv.runner import load_checkpoint
from mmdet.utils import get_root_logger

# from .utils import load_state_dict_from_url

__all__ = [
    "MobileNetV2_combined_no_relu_at_all",
    "mobilenet_v2_combined_no_relu_at_all",
]

model_urls = {
    "mobilenet_v2_combined_no_relu_at_all": "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
}


def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_combined_no_relu_at_all/mobilenet_combined_no_relu_at_all.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return int(v)


class ConvBNReLU(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(
                in_planes,
                out_planes,
                kernel_size,
                stride,
                padding,
                groups=groups,
                bias=False,
            ),
            nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True),
        )


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend(
            [
                # dw
                #            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
                nn.Conv2d(
                    hidden_dim, oup, 3, stride=stride, padding=1, groups=1, bias=False
                ),
                # pw-linear
                #            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            ]
        )
        self.conv = nn.Sequential(*layers)

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


@BACKBONES.register_module()
class MobileNetV2_combined_no_relu_origin(nn.Module):
    def __init__(
        self,
        num_classes=1000,
        width_mult=1.0,
        inverted_residual_setting=None,
        round_nearest=8,
        return_layers=["2", "4", "7", "14"],
        input_channel=32,
        last_channel=1280,
    ):
        """
        MobileNet V2 main class

        Args:
            num_classes (int): Number of classes
            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
            inverted_residual_setting: Network structure
            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
            Set to 1 to turn off rounding
        """
        super(MobileNetV2_combined_no_relu_origin, self).__init__()
        block = InvertedResidual
        #  input_channel = 32
        #  last_channel = 1280
        self.return_layers = return_layers

        if inverted_residual_setting is None:
            inverted_residual_setting = [
                # t, c, n, s
                [1, 16, 1, 1],
                [6, 24, 2, 2],
                [6, 32, 3, 2],
                [6, 64, 4, 2],
                [6, 96, 3, 1],
                [6, 160, 3, 2],
                [6, 320, 1, 1],
            ]

        # only check the first element, assuming user knows t,c,n,s are required
        if (
            len(inverted_residual_setting) == 0
            or len(inverted_residual_setting[0]) != 4
        ):
            raise ValueError(
                "inverted_residual_setting should be non-empty "
                "or a 4-element list, got {}".format(inverted_residual_setting)
            )

        # building first layer
        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
        self.last_channel = _make_divisible(
            last_channel * max(1.0, width_mult), round_nearest
        )
        features = [ConvBNReLU(3, input_channel, stride=2)]
        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(
                    block(input_channel, output_channel, stride, expand_ratio=t)
                )
                input_channel = output_channel
        # building last several layers
        features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
        # make it nn.Sequential
        self.features = nn.Sequential(*features)

        # building classifier
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(self.last_channel, num_classes),
        )

        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out")
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

    def forward(self, x):
        out = []
        return_layers = self.return_layers.copy()
        for name, module in self.features.named_children():
            if return_layers != []:
                x = module(x)
                if name in return_layers:  # [2,4,7,14]
                    out.append(x)
                    return_layers.remove(name)
            else:
                break
        #  x = self.features(x)
        #  x = x.mean([2, 3])
        #  x = self.classifier(x)
        return out

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)


#         else:
#             raise ValueError('No pretrained model!')


def mobilenet_v2_combined_no_relu_at_all(pretrained=False, progress=True, **kwargs):
    """
    Constructs a MobileNetV2_combined_no_relu_at_all architecture from
    `"MobileNetV2_combined_no_relu_at_all: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    model = MobileNetV2_combined_no_relu_at_all(**kwargs)
    if pretrained:
        #  state_dict = load_state_dict_from_url(model_urls['mobilenet_v2_combined_no_relu_at_all'],
        #                                        progress=progress)
        model.load_state_dict(state_dict)
    return model


class MobileNetV2_Wraper(nn.ModuleDict):
    def __init__(self, return_layers, **args):
        model = MobileNetV2_combined_no_relu_at_all(**args)
        orig_return_layers = return_layers
        return_layers = {k: v for k, v in return_layers.items()}
        layers = OrderedDict()
        for name, module in model.named_children():
            layers[name] = module
            if name in return_layers:
                del return_layers[name]
            if not return_layers:
                break
        if not set(return_layers).issubset(
            [name for name, _ in layers["features"].named_children()]
        ):
            raise ValueError("return_layers are not present in model")

        features = OrderedDict()
        for name, module in layers["features"].named_children():
            features[name] = module
            if name in return_layers:
                del return_layers[name]
            if not return_layers:
                break
        super(MobileNetV2_Wraper, self).__init__(features)
        self.return_layers = orig_return_layers

    def forward(self, x):
        out = []
        for name, module in self.named_children():
            x = module(x)
            if name in self.return_layers:  # [2,4,7,14]
                out_name = self.return_layers[name]
                out.append(x)
        import pdb

        pdb.set_trace()
        return out

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """
        if isinstance(pretrained, str):
            pass


if __name__ == "__main__":
    model = MobileNetV2_combined_no_relu_origin()
    # model = MobileNetV2_Wraper(model,{"4":1})
    import torch

    x = torch.ones(1, 3, 320, 320)
    y = model(x)
